User identification by biometric monitoring device

ABSTRACT

Techniques for user identification by a biometric monitoring device are disclosed. In one aspect, a method of operating a biometric monitoring device may involve measuring a weight of a user in response to detecting that the user is standing on a platform, determining, based on sensor data generated by a sensor, a user parameter indicative of the user&#39;s identification, identifying one of a plurality of user profiles as corresponding to the user based on a comparison of the user parameter to parameter values and a comparison of the measured weight of the user to weight values, and updating the identified user profile based on at least one of the measured weight and the user parameter.

INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently with this specificationas part of the present application. Each application that the presentapplication claims benefit of or priority to as identified in theconcurrently filed Application Data Sheet is incorporated by referenceherein in its entirety and for all purposes.

TECHNICAL FIELD

This disclosure relates to the field of biometric monitoring devices,and particularly to the identification of a user of a biometricmonitoring device.

BACKGROUND

Consumer interest in personal health has led to a variety of personalhealth monitoring devices being offered on the market. Such devices,until recently, tended to be complicated to use and were typicallydesigned for use with one activity, for example, bicycle trip computers.

Advances in sensors, electronics, and power source miniaturization haveallowed the size of personal health monitoring devices, also referred toherein as “biometric tracking,” “biometric monitoring,” or, in certainembodiments, simply “wearable” devices, to be offered in extremely smallsizes that were previously impractical. The number of applications forthese devices is increasing as the processing power and componentminiaturization for wearable devices improves.

Certain biometric monitoring devices may be configured to monitorbiometrics from a plurality of users. A user may be required to select acorresponding user profile to ensure that the biometric measurements areassociated with the correct user of the device. Thus, the user may berequired to perform certain interactions with the biometric monitoringdevice in order to have the measured biometrics associated with thecorrect user profile.

SUMMARY

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one aspect, there is provided a method of operating a biometricmonitoring device, the device comprising a platform configured toreceive at least one foot of a user, a plurality of sensors, a memory,and processing circuitry. The method may involve: detecting that theuser is standing on the platform; measuring, based on body-weight datagenerated by a first one of the sensors, a weight of the user inresponse to detecting that the user is standing on the platform; anddetermining, based on sensor data generated by a second one of thesensors, a user parameter indicative of the user's identification. Themethod may also involve: comparing the user parameter to a plurality ofparameter values respectively associated with a plurality of userprofiles stored in the memory; and comparing the measured weight of theuser to a plurality of weight values respectively associated with eachof the user profiles. The method may further involve: identifying theone of the user profiles as corresponding to the user based on thecomparison of the user parameter to the parameter values and thecomparison of the measured weight of the user to the weight values; andupdating the identified user profile based on at least one of themeasured weight and the user parameter.

In another aspect, there is provided a biometric monitoring device thatincludes: a platform configured to receive at least one foot of a user;a first sensor configured to generate body-weight data; a second sensorconfigured to generate sensor data; and a memory configured to store aplurality of user profiles, each user profile including a weight valueand a parameter value. The biometric monitoring device may furtherinclude processing circuitry configured to: detect that the user isstanding on the platform; measure, based on the body-weight datagenerated by the first sensor, a weight of the user in response todetecting that the user is standing on the platform; determining, basedon the sensor data generated by the second sensor, a user parameterindicative of the user's identification; comparing the user parameter toa plurality of parameter values respectively associated with a pluralityof user profiles; comparing the measured weight of the user to aplurality of weight values respectively associated with each of the userprofiles; identifying one of the user profiles as corresponding to theuser based on the comparison of the user parameter to the parametervalues and the comparison of the measured weight of the user to theweight values; and updating the identified user profile based on atleast one of the measured weight and the user parameter.

In yet another aspect, there is provided a method of operating abiometric monitoring device, the device comprising a platform configuredto receive at least one foot of a user, a body-weight sensor, abioelectrical impedance sensor, a memory, and processing circuitry. Themethod may involve: detecting that the user is standing on the platform;measuring, based output from the body-weight sensor, a weight of theuser in response to detecting that the user is standing on the platform;applying at least one electrical signal to the user's body; measuring,based on output from the bioelectrical impedance sensor, thebioelectrical impedance of the user's body based on the application ofthe at least one electrical signal; determining at least onebioelectrical impedance parameter associated with the user based on themeasured bioelectrical impedance of the user's body according to abioelectrical model of the user's body as a plurality of electricalelements; and identifying one of a plurality of user profiles ascorresponding to the user based on the determined at least onebioelectrical impedance parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating certain components of an examplebiometric monitoring device in accordance with aspects of thisdisclosure.

FIG. 1B is a block diagram illustrating example biometric sensors whichmay be in communication with a processor of a biometric monitoringdevice in accordance with aspects of this disclosure.

FIG. 1C is an example block diagram illustrating an ad-hoc communicationnetwork that may be used in determining a user profile to which acurrent body-weight measurement corresponds in accordance with aspectsof this disclosure.

FIG. 1D is a block diagram illustrating certain components of anotherexample biometric monitoring device in accordance with aspects of thisdisclosure.

FIGS. 2A-2D are example layout configurations of physiological sensors(e.g., foot pads) of a biometric monitoring device in accordance withaspects of this disclosure.

FIG. 3 is an example of a wrist-worn device in accordance with aspectsof this disclosure.

FIG. 4 is a flowchart illustrating a method for the automaticidentification of a user of a biometric monitoring device in accordancewith aspects of this disclosure.

FIG. 5 is a block diagram illustrating an example method for classifyingmeasurements in accordance with aspects of this disclosure.

FIG. 6 is a diagram illustrating the various paths through which currentmay flow through the human body in accordance with aspects of thisdisclosure.

FIG. 7 is a diagram illustrating a circuit diagram model of the currentpaths illustrated in FIG. 6.

FIG. 8 is a plot of a plurality of measurements for two independentvariables in accordance with aspects of this disclosure.

FIG. 9 is a flowchart illustrating an example method for useridentification in accordance with aspects of this disclosure.

DETAILED DESCRIPTION

One of the biometrics which may be measured and tracked by a biometricmonitoring device is the weight of a user. Since changes to a user'sweight outside of normal daily fluctuations occur at a relatively slowpace (e.g., on the order of days/weeks), a body-weight monitoring device(e.g., a scale) may be used by a user at relatively infrequent intervals(e.g., once a day). A user does not need to continually update theirweight measurements over the course of the day (e.g., a user does nottypically measure changes in weight over the course of a workout), andmultiple users can measure their weight by sharing use of the samebody-weight monitoring device.

When a plurality of users measure their respective body-weights usingthe same body-weight biometric monitoring device, it may be necessary todetermine to which user a particular weight measurement corresponds inorder to update the correct user's body-weight profile. One techniquefor distinguishing between users may involve receiving a selectionand/or confirmation from the user to identify the correct user profile.However, it may be desirable to automatically identify the user in orderto streamline the user's interaction with the body-weight biometricmonitoring device (e.g., by simplifying and/or streamlining useridentification and/or body weight measurement).

The difficulty in automatically distinguishing between two users may berelated to the difference in weight between the two users. For example,when the difference in weight between two users is less than apredetermined value (e.g., 5 pounds), the normal fluctuations in weightof the users may result in overlapping body-weight measurements for thetwo users. Accordingly, it may not be possible to determine to which ofthe users a particular measurement belongs based solely on thebody-weight measurement. Aspects of the present disclosure relate totechniques for identifying a user to which a particular body-weightmeasurement belongs by using additional information about the user.

Biometric Monitoring Device Overview

FIG. 1A is a block diagram illustrating an example biometric monitoringdevice in accordance with aspects of this disclosure. The biometricmonitoring device 100 may include a processor 120, a memory 130, awireless transceiver 140, and one or more biometric sensor(s) 160. Thebiometric monitoring device 100 may also optionally include a userinterface 110 and one or more environmental sensor(s) 150. Althoughcertain block(s) and/or step(s) may be described as optional inconnection with the description of the figures, depending on theembodiment, any one or more of the illustrated block(s) and/or step(s)may be optional (e.g., omitted) for certain embodiment(s). The wirelesstransceiver 140 may be configured to wirelessly communicate with aclient device 170 and/or server 175, for example, either directly orwhen in range of a wireless access point (not illustrated) (e.g., via apersonal area network (PAN) such as Bluetooth pairing, via a WLAN, viaIEEE 802.11 (Wi-Fi), via near field communication (NFC), via one of theIEEE 802.15.4, ANT+, and ISO 18000-6C standards, etc.). Examples of theclient device 170 include a wearable device (e.g., wearable device 302illustrated in FIG. 3), a mobile phone, wired or wireless headphones, amusic/media player (e.g., a portable music player), a camera, a weightscale, etc. Depending on the implementation, the client device 170 maybe any device capable of communicating with the biometric monitoringdevice 100. Each of the memory 130, the wireless transceiver 140, theone or more biometric sensor(s) 160, the user interface 110, and/or theone or more environmental sensor(s) 150 may be in electricalcommunication with the processor 120.

The memory 130 may store instructions for causing the processor 120 toperform certain actions. For example, the processor 120 may beconfigured to measure a body-weight of a user and automaticallydetermine to which of a plurality of user profiles the body-weightmeasurement corresponds. In some embodiments, the biometric sensors 160may include one or more of a body-weight sensor, a bioelectricalimpedance (also referred to as bio-impedance) sensor, an optical sensor(e.g., a photoplethysmographic (PPG) sensor), an accelerometer, afootprint scanner, and/or other biometric sensor(s). Further informationregarding such biometric sensors are described in more detail below(e.g., in connection with FIG. 1B).

The biometric monitoring device 100 may collect one or more types ofphysiological and/or environmental data from the one or more biometricsensor(s) 160, the one or more environmental sensor(s) 150, and/orexternal devices and communicate or relay such information to otherdevices (e.g., the client device 170 and/or the server 175), thuspermitting the collected data to be viewed, for example, using a webbrowser or network-based application. For example, while the user standson the biometric monitoring device 100, the biometric monitoring device100 may perform biometric monitoring via measuring the user'sbody-weight using the one or more biometric sensor(s) 160. The biometricmonitoring device 100 may transmit data representative of the user'sbody-weight to an account on a web service (e.g., www.fitbit.com),computer, mobile phone, and/or health station where the data may bestored, processed, and/or visualized by the user. The biometricmonitoring device 100 may measure or calculate other physiologicalmetric(s) in addition to, or in place of, the user's body-weight. Suchphysiological metric(s) may include, but are not limited to: body-fat;bio-impedance; heart rate; heartbeat waveform; heart rate variability;heart rate recovery; blood pressure; blood glucose; skin conduction;skin and/or body temperature; muscle state measured viaelectromyography; brain activity as measured by electroencephalography;weight; caloric intake; nutritional intake from food; medication intake;pH levels; hydration levels; respiration rate; and/or otherphysiological metrics.

The biometric monitoring device 100 may also measure or calculatemetrics related to the environment around the user (e.g., with the oneor more environmental sensor(s) 150), such as, for example, barometricpressure, weather conditions (e.g., temperature, humidity, pollen count,air quality, rain/snow conditions, wind speed), light exposure (e.g.,ambient light, ultra-violet (UV) light exposure, time and/or durationspent in darkness), noise exposure, radiation exposure, and/or magneticfield. Furthermore, the biometric monitoring device 100 (and/or theclient device 170 and/or the server 175) may collect data from thebiometric sensor(s) 160 and/or the environmental sensor(s) 150, and maycalculate metrics derived from such data. For example, the biometricmonitoring device 100 (and/or the client device 170 and/or the server175) may calculate the user's stress or relaxation levels based on acombination of heart rate variability, skin conduction, noise pollution,and/or sleep quality. In another example, the biometric monitoringdevice 100 (and/or the client device 170 and/or the server 175) maydetermine the efficacy of a medical intervention, for example,medication, based on a combination of data relating to medicationintake, sleep, and/or activity. In yet another example, the biometricmonitoring device 100 (and/or the client device 170 and/or the server22) may determine the efficacy of an allergy medication based on acombination of data relating to pollen levels, medication intake, sleepand/or activity. These examples are provided for illustration only andare not intended to be limiting or exhaustive.

FIG. 1B is a block diagram illustrating a number of example biometricsensors that may be in communication with the processor of the biometricmonitoring device in accordance with aspects of this disclosure. As usedherein, the term biometric sensor 160 may generally refer to any sensorthat senses or detects information about the user of the biometricmonitoring device 100, as opposed to, for example, an environmentalsensor 150 that senses or detects information about the environmentrather than the user. For example, in the embodiment of FIG. 1B, thebiometric monitoring device 100 may include one or more body-weightsensor(s) 166 which may be used to determine the body-weight of acurrent user of the biometric monitoring device 100. Examples of thebody-weight sensor 166 include a load sensor, a piezoelectric sensor, astrain gauge, etc. In an example implementation, the body-weightsensor(s) 166 include a plurality of load sensors located at differentpositions in the biometric monitoring device 100. By including aplurality of load sensors, for example, located adjacent to the cornersof the biometric monitoring device 100, the body-weight sensor(s) 166may be able to determine the user's weight distribution (e.g., a patternof motion of the user standing on a platform 195 of the biometricmonitoring device 100 (see FIG. 1D)), which may be indicative of acenter of the user's weight over the platform 195, while he/she isstanding on the platform 195 and/or how the user's weight distributionchanges while the user mounts the biometric monitoring device 100. Thewearable device may further include bio-impedance sensor(s) 162 whichmay be used to determine the mass of the body-fat of the current user.The biometric monitoring device 100 may further include optional opticalsensor(s) 168 (e.g., a PPG sensor), and may optionally include otherbiometric sensor(s) 164 such as directional sensor(s) (e.g., anaccelerometer). Examples of directional sensor(s) include theaccelerometer, gyroscopes, magnetometers, a 3-axis inertial-measurementunit (IMU), a 6-axis IMU, a 9-axis IMU, etc. For example, the 3-axis IMUmay be an accelerometer, the 6-axis IMU may be a combination of anaccelerometer and a gyroscope, and the 9-axis IMU may be a combinationof an accelerometer, a gyroscope, and a magnetometer. Each of thebiometric sensors illustrated in FIG. 1B is in electrical communicationwith the processor 120. The processor 120 may use input received fromany combination of the body-weight sensor(s) 166, the optical sensor(s)168, the bio-impedance sensor(s) 162, and/or the other biometricsensor(s) 164 in determining to which user profile a current body-weightmeasurement corresponds. In some embodiments, the body-weight sensor(s)166, the optical sensor(s) 168, the bio-impedance sensor(s) 162, and/orthe other biometric sensor(s) 164 may correspond to the biometricsensor(s) 160 illustrated in FIG. 1A.

Other examples of the biometric sensor(s) 160 include sensors to detect,measure and/or sense data which is representative of heart rate,respiratory rate, hydration, height, sun exposure, blood pressure and/orarterial stiffness. In addition thereto, or in lieu thereof, thebiometric monitoring device 100 may detect, measure and/or sense (viaappropriate sensors) other physiologic data. All such physiologic dataor parameters, whether now known or later developed, are intended tofall within the scope of this disclosure. Similarly, although thebiometric sensor(s) 160 may be depicted as independent, they may becollaborative and perform multiple types of measurements. For example, abody-weight sensor 166 in combination with bio-impedance sensor 162 maybe used to measure body fat, hydration, and/or fat-free mass.

It related aspects, the processor 120 and other component(s) of thebiometric monitoring device 100 (e.g., shown in FIGS. 1A and 1B) may beimplemented as any of a variety of suitable circuitry, such as one ormore microprocessors, application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs), discrete logic, software,hardware, firmware or any combinations thereof. When the techniques areimplemented partially in software, a device may store instructions forthe software in a suitable, non-transitory computer-readable medium andexecute the instructions in hardware using one or more processors toperform the techniques of this disclosure.

In further related aspects, the processor 120 and other component(s) ofthe biometric monitoring device 100 may be implemented as a SoC that mayinclude one or more CPU cores that use one or more reduced instructionset computing (RISC) instruction sets, a WWAN radio circuit, a WLANradio circuit, and/or other software and hardware to support thebiometric monitoring device 100.

FIG. 1C is an example block diagram illustrating an ad-hoc communicationnetwork that may be used in determining a user profile to which acurrent body-weight measurement corresponds in accordance with aspectsof this disclosure. As shown in FIG. 1C, a user is standing on abiometric monitoring device 100, is wearing a wearable device 170 onhis/her wrist (e.g., the wearable device 302 illustrated in FIG. 3), andis carrying a mobile device 170. The biometric monitoring device 100,the wearable device, and/or the client device 170 may be in wirelesscommunication with each other and/or a Wi-Fi router 185.

As described in more detail below, the biometric monitoring device 100may determine a parameter which is indicative of the identity of theuser based on sensor data generated by a sensor other than thebody-weight sensor(s) 166. The parameter may be a physiologicalparameter or a non-physiological parameter. The sensor data may begenerated by and/or received from at least one of the biometricmonitoring device 100, the wearable device 170, the mobile device 170,and the Wi-Fi router 185. For example, in one implementation thebiometric monitoring device 100 may receive the parameter indicative ofthe identity of the user from at least one of the wearable device 170,the mobile device 170, and the Wi-Fi router 185. Additionally, thebiometric monitoring device 100 may log each of the body-weightmeasurements into a corresponding user account stored on a server 175via the Wi-Fi router 185.

FIG. 1D a block diagram illustrating certain components of anotherexample biometric monitoring device in accordance with aspects of thisdisclosure. As shown in FIG. 1D, the biometric monitoring device 100 mayinclude a housing/platform 195 which houses the electronics andsensor(s) of the biometric monitoring device 100. The biometricmonitoring device 100 may further include a processor 120, a wirelesstransceiver 160, and one or more biometric sensor(s) 160. The biometricsensor(s) 160 may be housed within the platform 195 and communicate withthe processor 120 via a wired connection or may also be remote from theplatform 195 and communicate with the processor 120 via the wirelesstransceiver 160.

In the illustrated embodiment, the biometric monitoring device 100 mayalso include a pair of foot pads 190 and a user interface 110, which maybe positioned in the illustrated regions of the platform 195. Theplatform 195 may be configured to receive at least one foot of a user,for example, the platform 195 may be configured to receive the feet of auser at the respective foot pads 190. However, the described technologyis not so limited and the foot pad(s) and/or user interface 110 may belocated in other positions. For example, the biometric monitoring device100 may use a user interface of a client device 100 (e.g., a mobilephone) as an interface for communication with the user.

The user interface 110 of biometric monitoring device 100 may provide orfacilitate exchange of physiologic information and, in certainembodiments, other information or data. For example, biometricmonitoring device 100 may include one or more displays to presentphysiologic information including, for example, current information,historical information, and/or comparison like information, for example,current information in view of historical information. The historicalinformation or data may include, for example, historical body-weightand/or body-fat data measured by the biometric monitoring device 100(which may be stored internally to and/or externally from biometricmonitoring device 100), historical user activity data, food consumptiondata, and/or sleep data (which may be measured or monitored by otherpersonal and/or portable devices (for example, the wearable device 302illustrated in FIG. 3), historical user biometric or physiologic data(for example, heart rate, blood pressure, arterial stiffness, bloodglucose levels, cholesterol, duration of TV watching, duration of videogame play and/or mood). Such historical data may be presented inpictorial, graphical and/or textual form.

In addition to or in lieu of one or more displays, user interface 110may include one or more speakers to provide the user with suchphysiologic data or information (whether current, historical or both) inaural form.

The user interface 110 may also include an input mechanism to facilitateinput of, for example, user data, commands and/or selections. In oneembodiment, the user may communicate with biometric monitoring device110 via a user interface including, for example, a touch pad, touchscreen, buttons, switches and/or knobs. In another embodiment, userinterface 110 of a biometric monitoring device 100 includes one or moreaudio sensors to detect speech or sounds. In this way, for example, theuser may input data, commands and/or selections to biometric monitoringdevice 100. For example, in response to speech or sounds from the user,biometric monitoring device 100 may determine or identify theappropriate user (for example, from a list stored therein) whichfacilitates correlation of physiologic data (acquired by the one or morephysiological sensors) with a particular user.

Certain biometric sensor(s) 160 may be located directly beneath the footpads 190. For example, certain biometric sensor(s) 160 may generate asignal that is supplied to the skin of the user and measure a responsefrom the user in order to measure a corresponding biometric. As anexample, a PPG sensor may generate an optical signal which is applied tothe user's feet and may measure the light reflected from the user. Thisreflected signal may be used by the PPG sensor to measure one or more ofthe user's heart rate, heartbeat waveform; heart rate variability; heartrate recovery, etc. Similarly, a bio-impedance sensor may generate anelectrical signal which may be applied to the user through the user'sfeet or another part of the user's body connected to an electrode. Thebio-impedance sensor may measure a response to the applied electricalsignal and model certain parameters of the user's body based on themeasured signal. Further examples of the bio-impedance sensor arediscussed in detail below.

Foot Pads for Biometric Monitoring Device

FIGS. 2A-2D are example configurations of physiological sensor layoutsof foot pads of a biometric monitoring device 100 (for example,illustrated in FIG. 1D) having a plurality of physiological sensorsincluding a body-weight sensor 166, one or more LED-photo detector pairs210 and one or more bioelectrical impedance analysis (BIA) electrodes205. Depending on the implementation, one or more of the LED-photodetector pairs 210 and the bioelectrical impedance analysis (BIA)electrodes 205 may not be included.

In one embodiment, in operation, the processor 120 calculates ordetermines the user's weight based on or using data from the body-weightsensor 166 incorporated and/or embedded in foot pads. In addition, theprocessor 120 may employ the data from a bio-impedance sensor(s) 162, tocalculate or determine a user's body fat composition and/or body massindex. The bio-impedance sensor(s) 162 may comprise the BIA electrodes205, from which a small current may be applied to the user's body andthe characteristics of the return current measured in the electrodes maybe representative of the body fat composition of the user. The processor120, based on data acquired or detected by the BIA electrodes 205 anduser information (e.g., height, age, and gender), may calculate ordetermine a user's body fat composition and/or body mass index

With continued reference to FIGS. 2A-2D, the foot pads also include oneor more LED-photo detector pairs 210 arranged therein such that when theuser placing blood-perfused area of the foot (for example, the big toe)over the one or more LED-photo detector pairs 210, the biometricmonitoring device 100 may implement or perform photo plethysmography tocalculate, assess and/or determine blood pressure and/or arterialstiffness. For example, an array of LED-photo detector pairs 210 may beemployed to adaptively determine which location on the foot provides thebest plethysmography signal. Using data from the LED-photo detectorpairs 210, the processor 120 may calculate, assess and/or determineother biometric or physiological quantities such as heart rate, bloodpressure and/or arterial stiffness. That is, the processor 120 mayemploy data from a heart rate sensor to calculate, assess and/ordetermine the user's heart rate using, for example,ballistocardiography. Based on the output of such sensors, the processor120 may calculate, assess and/or determine the user's heart rate andstore and/or output such information (for example, to a display (userinterface) or external to biometric monitoring device 100 via wirelesstransceiver 140).

Wearable Device Overview

In certain embodiments, the client device 170 may comprise a wearabledevice 302 which may have a shape and/or size adapted for coupling to(e.g., secured to, worn, borne by, etc.) the body or clothing of a user.FIG. 3 shows an example of a wrist-worn wearable device 302 inaccordance with aspects of this disclosure. The wrist-worn wearabledevice 302 may have a display 305, button(s) 304, electronics package(not illustrated), and/or an attachment band 306. The attachment band306 may be secured to the user through the use of hooks and loops (e.g.,Velcro), a clasp, and/or a band having memory of its shape, for example,through the use of a spring metal band.

The wearable device 302 may collect one or more types of physiologicaland/or environmental data from one or more biometric sensor(s), one ormore environmental sensor(s), and/or external devices and communicate orrelay such information to other devices (e.g., the client device 170,the server 175, and or the biometric monitoring device 100), thuspermitting the collected data to be viewed, for example, using a webbrowser or network-based application. For example, while being worn bythe user, the wearable device 302 may perform biometric monitoring viacalculating and storing the user's step count using one or morebiometric sensor(s) included therein. The wearable device 302 maytransmit data representative of the user's step count to an account on aweb service (e.g., www.fitbit.com), computer, mobile phone, and/orhealth station where the data may be stored, processed, and/orvisualized by the user. The wearable device 302 may measure or calculateother physiological metric(s) in addition to, or in place of, the user'sstep count. Such physiological metric(s) may include, but are notlimited to: energy expenditure, e.g., calorie burn; floors climbedand/or descended; heart rate; heartbeat waveform; heart ratevariability; heart rate recovery; location and/or heading (e.g., via aGPS, global navigation satellite system (GLONASS), or a similar system);elevation; ambulatory speed and/or distance traveled; swimming lapcount; swimming stroke type and count detected; bicycle distance and/orspeed; blood pressure; blood glucose; skin conduction; skin and/or bodytemperature; muscle state measured via electromyography; brain activityas measured by electroencephalography; weight; body fat; caloric intake;nutritional intake from food; medication intake; sleep periods (e.g.,clock time, sleep phases, sleep quality and/or duration); pH levels;hydration levels; respiration rate; and/or other physiological metrics.

The wearable device 302 may also measure or calculate metrics related tothe environment around the user (e.g., with one or more environmentalsensor(s) included therein), such as, for example, barometric pressure,weather conditions (e.g., temperature, humidity, pollen count, airquality, rain/snow conditions, wind speed), light exposure (e.g.,ambient light, ultra-violet (UV) light exposure, time and/or durationspent in darkness), noise exposure, radiation exposure, and/or magneticfield. Furthermore, the wearable device 302 (and/or the client device170, the server 175 and/or the biometric monitoring device 100) maycollect data from biometric sensor(s) and/or the environmentalsensor(s), and may calculate metrics derived from such data. Forexample, the wearable device 302 (and/or the client device 170, theserver 175, and/or the biometric monitoring device 100) may calculatethe user's stress or relaxation levels based on a combination of heartrate variability, skin conduction, noise pollution, and/or sleepquality. In another example, the wearable device 302 (and/or the clientdevice 170, the server 175, and/or the biometric monitoring device 100)may determine the efficacy of a medical intervention, for example,medication, based on a combination of data relating to medicationintake, sleep, and/or activity. In yet another example, the wearabledevice 302 (and/or the client device 170, the server 175, and/or thebiometric monitoring device 100) may determine the efficacy of anallergy medication based on a combination of data relating to pollenlevels, medication intake, sleep and/or activity. These examples areprovided for illustration only and are not intended to be limiting orexhaustive.

User Identification

Certain aspects of this disclosure relate to the identification of auser currently using the biometric monitoring device 100. As discussedabove, since body-weight biometric monitoring devices 100 are nottypically used continually over extended periods of time (e.g., forlonger than 1 minute at a time), but rather are typically usedintermittently for shorter periods of time (e.g., less than 1 minute), asingle body-weight biometric monitoring device 100 may be used by aplurality of users. Aspects of this disclosure relate to techniques forautomatically determining to which user profile a given measurementtaken by the biometric monitoring device 100 corresponds.

While the embodiments described below generally relate to a body-weightbiometric monitoring device 100, the techniques may also be extended toother biometric monitoring devices, such as client device 170, which areshared between users. For example, two or more users may share awearable device, such as wearable device 302 in order to track certainexercise metrics. The use of sensor data generated by a secondary sensorin order to identify the current user of the biometric monitoring device100 as described below may also be applied to wearable device 302.

FIG. 4 is a flowchart illustrating an example method for the automaticidentification of a user of a biometric monitoring device in accordancewith aspects of this disclosure. The method 400 may be operable by abiometric monitoring device 100, or component(s) thereof, for useridentification in accordance with aspects of this disclosure. Forexample, the steps of method 400 illustrated in FIG. 4 may be performedby a processor 120 of the biometric monitoring device 100. In anotherexample, a client device 170 (e.g., a mobile phone) or a server 175 incommunication with the biometric monitoring device 100 may perform atleast some of the steps of the method 400. For convenience, the method400 is described as performed by the processor 120 of the wearabledevice 100.

The method 400 starts at block 401. At decision block 405, the processor120 detects whether a user is standing on a platform 195 of thebiometric monitoring device 100. In response to detecting that the useris not standing on the platform 195, the method 400 returns to the startof block 405. In response to detecting that the user is standing on theplatform 195, the method 400 proceeds to block 410, at which theprocessor 120 measures the body-weight of the user using a body-weightsensor 166. At optional decision block 415, the processor 120 determineswhether the measured body-weight matches two or more user profiles. Forexample, the processor 120 may determine whether the measuredbody-weight is within a defined range of two or more weight valuesassociated with two or more corresponding user profiles stored in thememory 130. In some embodiments, memory 130 may correspond to volatileor non-volatile storage. In some embodiments, the user profiles storedin the memory 130 may be transmitted to the body-weight biometricmonitoring device 100 from another device (e.g., a server) before,during, or after the body-weight of the user is measured at block 410.The weight values may be an average, mean, and/or other mathematicalcombination of the previous measured body-weight values for thecorresponding user profiles. For example, the processor 120 maydetermine an expected body-weight value for each user profile based on atrend in the historical measured body-weight values for the given user.

In other embodiments, the processor 120 may determine whether themeasured body-weight matches two or more user profiles by determiningwhether the measured body-weight is uniquely consistent with only one ofthe user profiles stored in the memory 130. For example, if the measuredbody-weight is not consistent with an expect amount of weightfluctuation since a previous body-weight measurement for a given userprofile, the processor 120 may determine that the body-weightmeasurement is not consistent with the given user profile.

In yet other embodiments, the method 400 may proceed directly from block410 to block 420. That is, the method 400 may include identifying a userprofile corresponding to the user based on the measured body-weight andsensor data from a second sensor. The identification of a user profilewill be described in greater detail below.

In response to the processor 120 determining that the measuredbody-weight matches two or more user profiles (yes from block 415) or inresponse to measuring the body-weight of the user (block 410), themethod continues at block 420. In response to the processor 120determining that the measured body-weight does not match two or moreuser profiles (no from block 415), the method 400 ends at block 435. Atblock 420, the processor 120 receives sensor data from a second sensor.For example, the processor 120 may receive sensor data from one or moreof the optical sensor(s) 168, the bio-impedance sensor(s) 162, theenvironmental sensor(s) 150, and/or the other biometric sensor(s) 164.

At block 425, the processor 120 determines one or more user parameter(s)indicative of the user's identity. The determining of the userparameter(s) may be based on a model of the user generated from thereceived sensor data. For example, the processor 120 may determine auser parameter indicative of the user's identity based on the receivedsensor data by fitting the received sensor data to an electrical circuitmodel. Examples of data from which the user parameter may be determinedinclude: bio-impedance data, footprint data, a unique client device 170identifier, body-weight distribution data, a time of day of the weightmeasurement, a material worn on the user's feet during the weightmeasurement, a shape of the user's feet, etc.

At block 430, the processor 120 identifies one of the user profiles ascorresponding to the user based on the parameter(s) and the measuredweight. For example, the processor 120 may classify the measuredbody-weight as corresponding to the identified user profile based on theuser parameter(s) matching the stored historic body-weight values forthe identified user profile. As described in detail below, the matchingof user parameter(s) to the stored historic body-weight values mayinvolve determining a likelihood that the measured body-weightcorresponds to each of the user profiles and selecting the user profilehaving the highest likelihood and/or confidence as the identified userprofile. The method 400 ends at block 435. It is noted that theembodiments of the present disclosure are not limited to or by theexample shown in FIG. 4, and other variations may be implemented withoutdeparting from the spirit of this disclosure.

Bio-Impedance

FIG. 5 is a block diagram illustrating an example method for classifyingmeasurements in accordance with aspects of this disclosure. The method500 may be operable by a biometric monitoring device 100, orcomponent(s) thereof, for user identification in accordance with aspectsof this disclosure. For example, the steps of method 500 illustrated inFIG. 5 may be performed by a processor 120 of the biometric monitoringdevice 100. In another example, a client device 170 (e.g., a mobilephone) or a server 175 in communication with the biometric monitoringdevice 100 may perform at least some of the steps of the method 500. Forconvenience, the method 500 is described as performed by the processor120 of the wearable device 100.

In the embodiment illustrated in FIG. 5, the method 500 may involvereceiving data from body-weight sensor(s) 166 and bio-impedancesensor(s) 162. However, in other embodiments, aspects of the presentdisclosure may relate to methods which receive data from body-weightsensor(s) 166 and data from one or more other biometric sensor(s) 160,environmental sensor(s) 150, and/or wireless transceiver 140.

The bio-impedance sensor(s) 162 may include, for example, at least twoelectrodes, and in certain embodiments, may include at least fourelectrodes. In one embodiment, a first electrode (a source electrode)and a second electrode (a sink electrode) together apply an electricalsignal to the user's body and a third electrode (a reference electrode)and a fourth electrode (a measurement point) together measure a responseto the applied electrical signal. The electrodes may be positioned atdifferent locations on the user's body such that the electrical signalflows through at least a portion of the user's body. In oneimplementation, the first and second electrodes may be located tocontact a first foot of the user and the third and fourth electrodes maybe located to contact a second foot of the user. In this implementation,the electrical signal may flow through the user's body between the feetof the user. In another implementation, the electrodes may be configuredto apply the electrical signal to one portion of a user's foot andreceive the electrical signal from another portion of the user's foot.For example, the first and second electrodes may be configured tocontact the front portion of the user's foot (e.g., near the user'stoes) while the third and fourth electrodes may be configured to contacta back portion of the user's foot (e.g., near the user's heel). Incertain embodiments, four electrodes may be provided for each of theuser's feet.

In other implementations, at least two of the electrodes may beconfigured to contact another portion of the user's body. For example,in certain implementations, the biometric monitoring device 100 mayinclude a handle (not illustrated) which the user may grab with one ormore of his/her hands. The first and second electrodes may be providedon the handle such that the electrical signal may be applied to the userand/or received from the user via the handle. Accordingly, theelectrical signal may be applied to and/or received from one or morehands of the user.

At block 510, the processor 120 receives bio-impedance data from one ormore bio-impedance sensor(s) 162. The processor 120 fits the receivedbio-impedance data into a model of the human body to determine one ormore parameters. The processor 120 may model the human body according tothe model circuit 700 illustrated in FIG. 7. FIG. 7 is a circuit diagrammodel of the human body in accordance with aspects of this disclosure.As shown in FIG. 7, the human body may be modeled as an electricalcircuit comprising two resistors R_(e) and R_(i) and a capacitor C. Thecircuit 700 may include an extracellular water pathway resistance R_(e),an intracellular water resistance R_(i), and a cell membrane capacitanceC. For certain applications, the human body may be modeled as anelectrical circuit where the extracellular water pathway resistanceR_(e) is formed in parallel with the intracellular water resistanceR_(i) and the cell membrane capacitance C. The equivalent impedance ofthe model circuit 700 may therefore be determined based on the followingequation:

$\begin{matrix}{Z = \frac{R_{e}\left( {R_{i} + \frac{1}{j\;\omega\; C}} \right)}{R_{e} + \left( {R_{i} + \frac{1}{j\;\omega\; C}} \right)}} & (1)\end{matrix}$

FIG. 6 is a diagram illustrating the various paths through which currentmay flow in connection with the circuit diagram model of FIG. 7. Asshown in FIG. 6, low frequency current may be deflected around the cellmembranes to flow through the extracellular water while high frequencycurrent may pass through the cell walls and the intracellular water toflow through the user's tissue via a more direct path. The change in thecurrent path may be modeled as proportional to the frequency of thecurrent. One such model is given by equation (1) above. For example, afirst current path 610 following the extracellular water pathway may berepresented by a resistance R_(e), while a second current path 620passing through the intracellular water and the cell membranes may berepresented by a parallel path including a resistance R_(i) and acapacitance C. The resistance R_(i) may a model of the intracellularwater while the capacitance C may a model of the cell membranes. Thevalues R_(e), R_(i) and C may be bioelectrical impedance parametersassociated with the bio-impedance measurements of the user's body.

Based on one or more impedance measurements using the bio-impedancesensor(s) 162, the processor 120 may determine the values R_(e), R_(i),and C corresponding to the current user of the biometric monitoringdevice. When a plurality of impedance measurements are taken, at leastthree of the applied electrical signals may have different frequencies.One implementation for determining the values R_(e), R_(i), and C from anumber of impedance measurements is shown below.

The magnitude of a given impedance measurement is given by:

$\begin{matrix}{{Z} = {\frac{R_{e}{{R_{i} + \frac{1}{j\;\omega\; C}}}}{{R_{e} + R_{i} + \frac{1}{j\;\omega\; C}}} = \frac{R_{e}\sqrt{R_{i}^{2} + \frac{1}{\omega^{2}C^{2}}}}{\sqrt{R_{e}^{2} + {2R_{e}R_{i}} + R_{i}^{2} + \frac{1}{\omega^{2}C^{2}}}}}} & (2)\end{matrix}$

Based on equation (2), an observable value may be defined as:

$\begin{matrix}{{{y \equiv {Z}^{2}} = {\frac{{C_{r}^{2}R_{e}^{2}R_{i}^{2}} + {R_{e}^{2}x}}{{C_{r}^{2}\left( {R_{e}^{2} + {2R_{e}R_{i}} + R_{i}^{2}} \right)} + x} = \frac{a + {bx}}{c + x}}},{{{where}\mspace{14mu} x} \equiv \frac{1}{\omega^{2}C_{nom}^{2}}}} & (3)\end{matrix}$

Here, C_(nom) is a scaling factor which affects the stability of theequation and may be selected to aid in solving equation (3). Bymultiplying each side of equation (3) by (c+x), the equation can bealtered to the form:yx=a+bx−cy  (4)

Each of a plurality of measurements may be entered into equation (4) togenerate a system of equations, which can be generalized by thefollowing equation:

$\begin{matrix}{{y^{i}x^{i}} = {\left\lbrack {{1x^{i}} - y^{i}} \right\rbrack\begin{bmatrix}a \\b \\c\end{bmatrix}}} & (5)\end{matrix}$

From equation (5), the values represented by the vector [a b c]^(T) maybe solved for using, for example, a least squares approach, from whicheach of the values R_(e), R_(i) and C may be recovered.

Depending on the implementation, the bio-impedance sensor(s) 162 may beconfigured to apply one or more electrical signals to the user's feet,which may have differing frequencies. For example, since the user's bodymay be modeled as shown in FIG. 7 as an electrical circuit includingthree electrical components, the accuracy of the determination of thevalues R_(e), R_(i) and C may be increased by applying a greater numberof electrical signals having different frequencies to make a greaternumber of measurements Z. However, embodiments of this disclosure mayalso relate to the application of a single electrical signal having onefrequency.

In certain implementations, the bio-impedance sensor(s) 162 may apply a“white-noise” signal to the user and measure the electrical signalresponse. As used herein, a white-noise signal may refer to anelectrical signal having a substantially uniform frequency distributionover a defined range of frequencies. In certain implementationsemploying a white-noise signal, rather than calculating values R_(e),R_(i), and C according to the model of FIG. 7, the processor 120 may logthe electrical response signal as a parameter for user identification.For example, while the white-noise response signal may not necessarilycontribute to an electrical circuit model of the human body, thewhite-noise response signal may be unique for different users, and thus,may be used by the processor 120 to construct a parameter history foruser identification.

The user classification step 520 of FIG. 5 may be performed using thevalues R_(e), R_(i), and C along with the measured weight W. In oneimplementation, the processor 120 may format these values as a vector(Z^((i)))=[W, R_(e), R_(i), C]′. The processor 120 may maintain a userprofile for each of the users of the biometric monitoring device 100 inthe memory 130. The user profile may be generated based on the historyof measurements Z for the corresponding user. The user profile,including the history of measurements Z, may further be represented by anormal distribution for each of the parameters in the measurements Z,that is, the user profile may be represented by a multivariate normaldistribution, which may be defined by its mean value μ and a covariancematrix Σ.

The relationship between a given measurement Z^((i)) and a user profilemay be described by the Mahalanobis distance, which may be defined asthe measure of the distance between the measurement Z^((i)) and the meanμ of the user profile measurement history distribution, as scaled by thestandard deviation. Mahalanobis distances may represent the likelihoodthat the measurement Z^((i)) belongs to the user profile. That is, asample measurement having a first Mahalanobis distance to the mean μ₁ ofa first user profile than is less than a second Mahalanobis distance tothe mean μ₂ of a second user profile is more likely to belong to thefirst user profile than the second user profile. The Mahalanobisdistance for a given measurement (e.g., a k^(th) measurement) may bedetermined according to the following equation:

$\begin{matrix}{D_{k}^{(i)} = \sqrt{\left( {Z^{(i)} - \mu_{k}} \right)^{T}{\sum\limits_{k}^{- 1}\left( {Z^{(i)} - \mu_{k}} \right)}}} & (6)\end{matrix}$

FIG. 7 is a plot of a plurality of measurements for two independentvariables in accordance with aspects of this disclosure. In the exampleof FIG. 6, a plurality of Mahalanobis distance ellipses are overlaid toillustrate values of the standard deviation from the mean μ_(k) of themeasurements. The likelihood of a given measurement Z^((i)) belonging toa user profile k may be given by the following equation:

$\begin{matrix}{{L\left( {{Z^{i}❘\mu_{k}},\sum\limits_{k}} \right)} = {\left( {\left( {2\pi} \right)^{d}*{\sum\limits_{k}}} \right)^{- \frac{1}{2}}*{\exp\left( {{- \frac{1}{2}}D_{k}^{{(i)}2}} \right)}}} & (7)\end{matrix}$

Additionally, a confidence that the given measurement Z^((i)) belongs toa user profile k may be defined as:

$\begin{matrix}{{P\left( {Z^{(i)} \in Z_{k}} \right)} = \frac{L\left( {{Z^{(i)}❘\mu_{k}},\sum\limits_{k}} \right)}{\sum\limits_{j}{L\left( {{Z^{(i)}❘\mu_{j}},\sum\limits_{j}} \right)}}} & (8)\end{matrix}$

The processor 120 may calculate a confidence P for each of the userprofiles k that the given measurement Z^((i)) belongs to thecorresponding user profiles k. The processor 120 may then determinewhether the given measurement Z^((i)) belongs to one of the userprofiles k based on the determined confidences P.

After a user profile k has been identified, the processor 120 may updatethe corresponding user profile history, as shown in block 540 of FIG. 5.The processor 120 may use a Kalman filter to update the identified userprofile k with the current measurement Z^((i)). The use of the Kalmanfilter by the processor 120 may include a prediction of the currentstate of the user profile k using a dynamics model and processing thenoise covariance of the user profile. The use of the Kalman filter mayfurther involve updating the user profile k to correct the predictedcurrent state using a measurement of noise information.

Weight Distribution

While the method of FIG. 5 was described above in connection with theuse of bio-impedance measurements to determine the identity of the userof the biometric monitoring device 100, other implementations of thepresent disclosure relate to the use of alternative sensors/sources ofdata to determine the identity of the user. For example, in oneimplementation, the processor 120 may use additional data from the loadsensors of the body-weight sensor(s) 166 to determine the identity ofthe user. In one implementation, the weight sensor(s) 166 comprise aplurality of load sensors (e.g., four load sensors) positioned under theplatform 195 to track the position of the weight distribution on theplatform 195. The number of load sensors may be less than or greaterthan four in other embodiments. Various different aspects of the user'sweight distribution while the user is interacting with the biometricmonitoring device 100 may be used by the processor to identify the userprofile which corresponds to the user. For example, different aspects ofthe monitored weight distribution may be used as parameters by theprocessor 120 in distinguishing between user profiles, similar to thebio-impedance of the embodiments discussed in connection with FIGS. 5 to7.

In one implementation, the processor 120 may track the weightdistribution over the platform 195 as the user mounts the platform 195.This may involve tracking a position of the weight distribution (e.g., acenter) from a measurement of zero body-weight until movement of theweight distribution settles to within a tolerance range for movement.That is, the weight distribution may be logged until changes in theweight distribution are less than a threshold change in distribution forlonger than a predetermined period of time. Certain users may have apattern in his/her mounting weight distribution which may distinguishthe users from others. For example, one user may consistently mount theplatform 195 with his/her left foot first before putting his/her rightleg on the platform 195. Other mounting habits, such as an averageweight distribution which is further forward, backward, left, and/orright compared to other users may be used to distinguish between users.The speed at which stabilization in the weight distribution is achievedmay also be used to distinguish between users.

The processor 120 may also log the weight distribution of the user afterthe weight distribution has achieved stability (e.g., changes in theposition of the weight distribution are less than the threshold changein distribution for longer than the predetermined period of time). Dueto user preferences in position over the platform 195 and user posturehabits, the location of the weight distribution after stabilization maynot be centered on the platform 195. Thus, in certain implementations,the user profile may include a history measurements of the stabilizedposition of the center of the user's weight distribution. The weightdistribution history may be analyzed for classification of the usermeasurement similar to the bio-impedance measurements as discussed inconnection with FIG. 8.

In certain implementations, the body-weight sensor(s) 166 may furtherinclude one or more high-resolution pressure sensor(s). Thehigh-resolution pressure sensor(s) may have a resolution that candetermine an outline and/or pressure profile of the user's feet.Accordingly, the processor 120 may use one or more of the outline andpressure profile of the user's feet in determining the identity of theuser.

Wireless Communication and Other User Identification Parameters

The processor 120 may also use other parameters in the identification ofthe user in addition to or in place of those discussed above. Further,in certain embodiments, the parameters may not be physiologicalparameters. For example, the processor 120 may use signals received viathe wireless transceiver 140 to determine that a particular user iscurrently using the biometric monitoring device 100. That is, users ofthe biometric monitoring device 100 may wear and/or carry client devices170, such as a wearable device 302 or a mobile phone which areassociated with a particular user profile. These client devices 170 maywirelessly communicate with the processor 120 via the wirelesstransceiver 140 when within wireless communications range (e.g., thewearable device 302 and/or mobile phone may communicate with thewireless transceiver 140 via Bluetooth, Wi-Fi, NFC, etc.). Since one ormore client device(s) 170 may be uniquely associated with a particularuser, the processor 120 may identify the user of the biometricmonitoring device 100 when the biometric monitoring device 100 is incommunication with the client device(s) 170 while the biometricmonitoring device 100 is measuring the body-weight of the user.

In one example, the processor 120 may establish wireless communicationwith a client device 170 when the user approaches the biometricmonitoring device 100 and the user may subsequently mount the platform195. After the user has completed measurement of his/her body-weight,the user may leave the wireless communication range of the wirelesstransceiver 140, thereby terminating communication between the processor120 and the client device 170. Since the client device 170 was inwireless communication with the processor 120 during the measurement ofthe user's body-weight, the processor 120 may base the determination ofthe user profile corresponding to the user based on a unique identifierof the client device 170 (e.g., a device identifier such as media accesscontrol (MAC) address). The processor 120 may use the proximity of theclient device 170 as a parameter for determining the user'sidentification. For example, the strength of the wireless communicationsignal between the client device 170 and the wireless transceiver 140may be dependent on the distance between the client device 170 and thewireless transceiver 140. Accordingly, when two or more client devices170 are in wireless communication with the wireless transceiver 140, theprocessor 120 may use the unique identifier of the client device 170which is in closer proximity to the wireless transceiver 140 indetermining the identity of the user.

Another parameter which may be used by the processor 120 in identifyingthe user is a footprint measurement of the user. For example, thebiometric monitoring device 100 may include one or more opticalsensor(s) 168, such as a footprint scanner. The footprint scanner mayfunction similar to a fingerprint scanner and identify patterns in theridges of the skin of the user's feet which may be used to identify theuser. Depending on the implementation, the biometric monitoring device100 may include two footprint scanners over in locations where the userplaces his/her feet when standing on the platform (see the footpads 190of FIG. 1D). The footprint scanner may also identify when the user iswearing socks and/or shoes. Alternatively, the bio-impedance sensor(s)162 may determine whether the user is wearing a material on his/her feetor whether the user has bare feet. The tendencies of the user to wearsocks/shoes or use the scale with bare feet may be used as a parameterby the processor 120 to identify the user.

In certain implementations, the optical sensor(s) 168 may comprise a PPGsensor. The PPG sensor may be able to measure biometrics such as heartrate, heartbeat waveform, heart rate variability, heart rate recovery,etc. In other implementations, the bio-impedance sensor(s) 166 may beable to determine heart rate and/or blood pressure from thebio-impedance measurement. Any one of the parameters measured by opticalsensor(s) 168 and/or bio-impedance sensor(s) 166 may be used by theprocessor 120 to determine the identity of the user.

In other implementations, the biometric monitoring device 100 mayfurther include or be in wireless communications with a camera (e.g.,the camera may be included in a smart mirror in wireless communicationwith the biometric monitoring device 100). The camera may be configuredto identify users of the biometric monitoring device 100 based on facialrecognition algorithms and/or other image processing that issubstantially unique to the users of the biometric monitoring device100. The processor 120 may use the facial recognition algorithms of thecamera as a parameter in determining the identity of the user.

Automatic Determination of User Identification Parameters

A number of parameters which may be used by the processor 120 toidentify the user of the biometric monitoring device 100 when the useris measuring his/her body-weight have been described. However, dependingon the particular users who share a given biometric monitoring device100, certain parameters may be more accurate in distinguishing betweenthe users. Accordingly, in certain implementations, the processor 120may identify those parameters which are more accurate in distinguishingand identifying the users of the biometric monitoring device 100 andtrack the histories of the identified parameters in the user profilesfor user identification.

For example, the processor 120 may review the histories for each of theuser profiles associated with the biometric monitoring device 100 todetermine which user weight distribution parameters (e.g., habits) maybe useful in distinguishing between the users of the biometricmonitoring device. In one instance, a first user may habitually mountthe biometric monitoring device 100 with his/her left foot and have astabilized weight distribution which is forward from the center of theplatform 195. A second user may habitually mount the biometricmonitoring device 100 with his/her right foot and have a stabilizedweight distribution which is to the right from the center of theplatform 195. In this example, the processor 120 may identify theinitial mounting foot and the stabilized weight distribution location asbeing parameters which may be used to distinguish between the users. Theprocessor 120 may automatically identify users of the biometricmonitoring device 100 based on the identified parameters.

As another example, the processor 120 may identify significant overlapin the bio-impedance histories stored in corresponding user profiles fortwo users. This overlap may make it difficult to distinguish between thetwo users when using the biometric monitoring device 100. In thisexample, the processor 120 may select one or more other parameters foridentifying users including, for example, bio-impedance data, footprintdata, a unique client device 170 identifier, body-weight distributiondata, a time of day of the weight measurement, a material worn on theuser's feet during the weight measurement, a shape of the user's feet.

Example Flowchart for User Identification By Biometric Monitoring Device

FIG. 9 is a flowchart illustrating an example method operable by abiometric monitoring device 100, or component(s) thereof, for useridentification in accordance with aspects of this disclosure. Forexample, the steps of method 900 illustrated in FIG. 9 may be performedby a processor 120 of the wearable device 100. In another example, aclient device 170 (e.g., a mobile phone or wearable device) or a server175 in communication with the biometric monitoring device 100 mayperform at least some of the steps of the method 900. For convenience,the method 900 is described as performed by the processor 120 of thebiometric monitoring device 100.

In one implementation, the wearable device 100 comprises a platform 195configured to receive at least one foot of a user, a plurality ofsensors 140, 150, 160, a memory 130, and a processor (also referred toas processing circuitry) 120. The method 900 begins at block 901. Atblock 905, the processor 120 detects that the user is standing on theplatform 195. At block 910, the processor 120 measures, based onbody-weight data generated by a first one of the sensors, a weight ofthe user in response to detecting that the user is standing on theplatform 195.

At block 915, the processor 120 determines, based on sensor datagenerated by a second one of the sensors, a user parameter indicative ofthe user's identification. In some embodiments, the determination of theparameter indicative of the user's identification may be in response todetermining that the measured weight is within a defined range of two ormore weight values. At block 920, the processor 120 compares the userparameter to a plurality of parameter values respectively associatedwith a plurality of user profiles stored in the memory. At block 925,the processor 120 compares the measured weight of the user to aplurality of weight values respectively associated with each of the userprofiles. At block 930, the processor 120 identifies one of the userprofiles as corresponding to the user based on the comparison of theuser parameter to the parameter values and the comparison of themeasured weight of the user to the weight values. At block 935, theprocessor 120 updates the identified user profile based on at least oneof the measured weight and the user parameter. The method 900 ends atblock 940. It is noted that the embodiments of the present disclosureare not limited to or by the example shown in FIG. 9, and othervariations may be implemented without departing from the spirit of thisdisclosure.

OTHER CONSIDERATIONS

Information and signals disclosed herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the above description may berepresented by voltages, currents, electromagnetic waves, magneticfields or particles, optical fields or particles, or any combinationthereof.

The various illustrative logical blocks, and algorithm steps describedin connection with the embodiments disclosed herein may be implementedas electronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof. Such techniques may beimplemented in any of a variety of devices, such as, for example,wearable devices, wireless communication device handsets, or integratedcircuit devices for wearable devices, wireless communication devicehandsets, and other devices. Any features described as devices orcomponents may be implemented together in an integrated logic device orseparately as discrete but interoperable logic devices. If implementedin software, the techniques may be realized at least in part by acomputer-readable data storage medium comprising program code includinginstructions that, when executed, performs one or more of the methodsdescribed above. The computer-readable data storage medium may form partof a computer program product, which may include packaging materials.The computer-readable medium may comprise memory or data storage media,such as random access memory (RAM) such as synchronous dynamic randomaccess memory (SDRAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), electrically erasable programmable read-onlymemory (EEPROM), FLASH memory, magnetic or optical data storage media,and the like. The techniques additionally, or alternatively, may berealized at least in part by a computer-readable communication mediumthat carries or communicates program code in the form of instructions ordata structures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

Processor(s) in communication with (e.g., operating in collaborationwith) the computer-readable medium (e.g., memory or other data storagedevice) may execute instructions of the program code, and may includeone or more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, ASICs, field programmable logicarrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Such a processor may be configured to perform any of thetechniques described in this disclosure. A general purpose processor maybe a microprocessor; but in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,for example, a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Accordingly, the term“processor,” as used herein may refer to any of the foregoing structure,any combination of the foregoing structure, or any other structure orapparatus suitable for implementation of the techniques describedherein. Also, the techniques could be fully implemented in one or morecircuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wearable device, a wirelesshandset, an integrated circuit (IC) or a set of ICs (e.g., a chip set).Various components, or units are described in this disclosure toemphasize functional aspects of devices configured to perform thedisclosed techniques, but do not necessarily require realization bydifferent hardware units. Rather, as described above, various units maybe combined in a hardware unit or provided by a collection ofinter-operative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Although the foregoing has been described in connection with variousdifferent embodiments, features or elements from one embodiment may becombined with other embodiments without departing from the teachings ofthis disclosure. However, the combinations of features between therespective embodiments are not necessarily limited thereto. Variousembodiments of the disclosure have been described. These and otherembodiments are within the scope of the following claims.

What is claimed is:
 1. A method of operating a biometric monitoringdevice, the device comprising a platform configured to receive at leastone foot of a user, a plurality of sensors, a memory, and processingcircuitry, the method comprising: detecting that the user is standing onthe platform; measuring, in response to detecting that the user isstanding on the platform and based on body-weight data generated by afirst one of the sensors, a weight of the user; determining, in responseto detecting that the user is standing on the platform and based onsensor data generated by a second one of the sensors, a user parameterindicative of the user's identification, wherein the second sensorcomprises a wireless communication transceiver, and wherein thedetermining the user parameter comprises determining that a deviceassociated with the user is in the proximity of the wirelesscommunication transceiver; comparing the user parameter to a pluralityof parameter values respectively associated with a plurality of userprofiles stored in the memory; comparing the measured weight of the userto a plurality of weight values respectively associated with each of theuser profiles; identifying the one of the user profiles as correspondingto the user based on the comparison of the user parameter to theparameter values and the comparison of the measured weight of the userto the weight values; and updating the identified user profile based onat least one of the measured weight and the user parameter.
 2. Themethod of claim 1, wherein: the second sensor further comprises abiometric sensor; and the user parameter further comprises aphysiological parameter of the user.
 3. The method of claim 2, whereinthe determining of the physiological parameter comprises measuring abioelectrical impedance of the user's body.
 4. The method of claim 3,wherein the measuring of the user parameter comprises: applying at leastone electrical signal to the user's body; and measuring thebioelectrical impedance of the user's body based on the application ofthe at least one electrical signal.
 5. The method of claim 4, furthercomprising: determining a plurality of bioelectrical impedanceparameters associated with the user based on the measured bioelectricalimpedance of the user's body according to a bioelectrical model of theuser's body as a plurality of electrical elements, wherein thedetermining that the user parameter matches a parameter value associatedwith one of the user profiles is based on the determined bioelectricalimpedance parameters.
 6. The method of claim 5, wherein the parametervalue associated with each of the user profiles comprises a mean vectorand a covariance matrix of previously determined bioelectrical impedanceparameters associated with the user profile, and wherein the determinedbioelectric parameters are arranged in an observation vector, the methodfurther comprising: determining a Mahalanobis distance between i) theobservation vector and ii) each of the parameter values based on thecorresponding mean vector and the corresponding covariance matrix,wherein the identifying of the one of the user profiles as correspondingto the user is based on the determined Mahalanobis distances.
 7. Themethod of claim 1, wherein the device associated with the user comprisesa wearable electronic device.
 8. The method of claim 1, wherein thedetecting of the proximity of the device associated with the usercomprises detecting a device identifier of the device associated withthe user.
 9. The method of claim 1, wherein the wireless communicationtransceiver is configured to communicate with the device associated withthe user via at least one of: Bluetooth, IEEE 802.11 (Wi-Fi), near fieldcommunication (NFC), IEEE 802.15.4, ANT+, and ISO 18000-6C.
 10. Themethod of claim 1, wherein the first sensor comprises a firstbody-weight sensor and the second sensor comprises a second body-weightsensor.
 11. The method of claim 10, wherein the user parameter comprisesa pattern of motion of the user standing on the platform.
 12. The methodof claim 11, wherein the determining of the user parameter comprisesdetecting, using the first and second body-weight sensors, variations ina location of a center of the user's body-weight distribution as theuser steps onto the platform.
 13. The method of claim 11, wherein thedetermining of the user parameter comprises: detecting that a locationof the center of the user's body-weight distribution has stabilized towithin a tolerance range for movement; and determining, using the firstand second body-weight sensors, variations in the location of the user'sweight in response to detecting that the location of the user's weighthas stabilized.
 14. The method of claim 10, wherein each of the firstand second body-weight sensors comprises at least one load cell.
 15. Themethod of claim 1, wherein the user parameter comprises at least one of:a time of day of the weight measurement, a material worn on the user'sfeet during the weight measurement, a shape of the user's feet, and alocation of the user's center of gravity with respect to the platform.16. The method of claim 1, further comprising: determining that themeasured weight is within a defined range of two or more weight valuesrespectively associated with two or more user profiles stored in thememory, wherein determining a user parameter indicative of the user'sidentification is performed in response to determining that the measuredweight is within the defined range of the two or more weight values. 17.A biometric monitoring device, comprising: a platform configured toreceive at least one foot of a user; a first sensor configured togenerate body-weight data; a second sensor configured to generate sensordata; a memory configured to store a plurality of user profiles, eachuser profile including a weight value and a parameter value; andprocessing circuitry configured to: detect that the user is standing onthe platform; measure, in response to detecting that the user isstanding on the platform and based on the body-weight data generated bythe first sensor, a weight of the user; determining, in response todetecting that the user is standing on the platform and based on thesensor data generated by the second sensor, a user parameter indicativeof the user's identification, wherein the second sensor comprises awireless communication transceiver, and wherein the determining the userparameter comprises determining that a device associated with the useris in the proximity of the wireless communication transceiver; comparingthe user parameter to a plurality of parameter values respectivelyassociated with a plurality of user profiles; comparing the measuredweight of the user to a plurality of weight values respectivelyassociated with each of the user profiles; identifying one of the userprofiles as corresponding to the user based on the comparison of theuser parameter to the parameter values and the comparison of themeasured weight of the user to the weight values; and updating theidentified user profile based on at least one of the measured weight andthe user parameter.
 18. The device of claim 17, wherein the deviceassociated with the user comprises a wearable electronic device.
 19. Thedevice of claim 17, wherein the detecting of the proximity of the deviceassociated with the user comprises detecting a device identifier of thedevice associated with the user.
 20. The device of claim 17, wherein thewireless communication transceiver is configured to communicate with thedevice associated with the user via at least one of: Bluetooth, IEEE802.11 (Wi-Fi), near field communication (NFC), IEEE 802.15.4, ANT+, andISO 18000-6C.